Risk prediction models for postoperative infections in patients with hip fractures: a systematic review and critical appraisal
TL;DR
Current predictive models generally demonstrate good overall predictive performance; however, most models suffer from issues such as single-center development, insufficient external validation, and methodological limitations.
Abstract
Background To systematically evaluate the quality and performance of predictive models for postoperative infection risk following hip fractures, to identify reliable tools for clinical practice and provide an evidence-based foundation for the development of higher-quality predictive models in the future. Methods A systematic search was conducted on nine databases to retrieve relevant publications, from their inception up to 1 February 2026. Two researchers independently screened the literature and extracted data. They assessed the model bias and applicability using the Predictive Model Risk of Bias Assessment Tool (PROBAST) and the Checklist for Reporting on Multivariate Predictive Models for Individual Prognosis or Diagnosis-Artificial Intelligence (TRIPOD+AI). Results A total of 17 articles were included, covering 21 predictive models, with postoperative infection rates ranging from 1.61 to 24.56%. A meta-analysis of 11 high-frequency predictive factors revealed that hypoproteinemia, diabetes, pulmonary disease, ASA classification, smoking, indwelling catheter duration, age, and surgical duration were independent risk factors, while gender and albumin were not statistically significant. Furthermore, the area under the curve (AUC) for the included models ranged from 0.699 to 0.946. While most models performed well, all 17 studies were rated as having a high risk of bias by PROBAST, and the reporting quality of all studies according to TRIPOD+AI was relatively low, primarily due to retrospective study designs, regional bias, inadequate data analysis, insufficient external validation, and a lack of transparency in the research process. Conclusion Current predictive models generally demonstrate good overall predictive performance; however, most models suffer from issues such as single-center development, insufficient external validation, and methodological limitations. In the future, more multicenter, large-sample prospective studies should be conducted, and strategies for variable handling and model validation should be optimized to improve the generalizability and clinical translation of predictive models. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261289616, identifier (CRD420261289616).